Assessing the clinical application of deep-learning-derived CT volumetric measures in neurodegenerative disease diagnostics

BackgroundExisting bio fluid and imaging biomarkers used in research and clinical diagnostics of neurodegenerative diseases are often expensive or invasive and are mainly available in specialised care centres. CT is an affordable and widely available imaging modality predominantly used to evaluate structural abnormalities, but not for the volumetric quantification of neurodegeneration. Previously, we developed a deep learning model trained on MRI segmentations from individuals with paired CT and MR scans, which achieved high accuracy and robust tissue classification based on brain CT images. PurposeTo explore the diagnostic utility of deep-learning-derived CT-based atrophy measures and study their association with relevant cognitive, biochemical and other imaging markers of neurodegenerative diseases. Materials and methodsIn this retrospective study, we analysed 917 CT and 744 MR scans from cognitively healthy participants of the Gothenburg H70 Birth Cohort (70.4 {+/-} 2.6 years) and 204 CT and 241 MR scans from participants of the Memory Clinic Cohort, Singapore (73 Alzheimers disease, 20 vascular dementia, 22 cognitively normal; 74.0 {+/-} 8.2 years). We tested associations between six CT-derived volumetric measures with clinical diagnosis, fluid and imaging biomarkers and cognition. ResultsIn the Memory Clinic Cohort, deep-learning-derived CT-based atrophy measures differentiated cognitively healthy individuals from Alzheimers disease (AUC 0.88; 95% CI: 0.79-0.96) and vascular dementia (AUC 0.91; 95% CI: 0.81-1.00) patients with high accuracy levels comparable to MR-derived measures. Additionally, CT-based measures distinguished early, prodromal Alzheimers disease (AUC= 0.73, 95% CI: 0.62, 0.85) and prodromal vascular dementia patients from healthy individuals (CT-GM: AUC= 0.7, 95% CI: 0.51, 0.81). CT-derived volumes were significantly associated with measures of cognition and biochemical markers of neurodegeneration, notably plasma-derived neurofilament light ({rho}=-0.43, p<0.001, in the Memory Clinic Cohort). ConclusionOur findings provide strong evidence for the potential of deep-learning-derived CT-based atrophy measures in aiding neurodegenerative disease diagnostics in primary care settings.